Amidst the rapid growth of fashion e-commerce, remote fitting of fashion articles remains a complex and challenging problem and a main driver of customers' frustration. Despite the recent advances in 3D virtual try-on solutions, such approaches still remain limited to a very narrow - if not only a handful - selection of articles, and often for only one size of those fashion items. Other state-of-the-art approaches that aim to support customers find what fits them online mostly require a high level of customer engagement and privacy-sensitive data (such as height, weight, age, gender, belly shape, etc.), or alternatively need images of customers' bodies in tight clothing. They also often lack the ability to produce fit and shape aware visual guidance at scale, coming up short by simply advising which size to order that would best match a customer's physical body attributes, without providing any information on how the garment may fit and look. Contributing towards taking a leap forward and surpassing the limitations of current approaches, we present FitGAN, a generative adversarial model that explicitly accounts for garments' entangled size and fit characteristics of online fashion at scale. Conditioned on the fit and shape of the articles, our model learns disentangled item representations and generates realistic images reflecting the true fit and shape properties of fashion articles. Through experiments on real world data at scale, we demonstrate how our approach is capable of synthesizing visually realistic and diverse fits of fashion items and explore its ability to control fit and shape of images for thousands of online garments.
翻译:在时装电子商务的迅速增长中,时装文章的远程安装仍然是一个复杂而富有挑战性的问题,也是客户沮丧的主要驱动力。尽管最近3D虚拟试镜解决方案的进展,但这类方法仍然局限于非常狭窄的选择 — — 甚至只是少数的 — — 文章的选择,而且往往只针对这些时装项目中的一种尺寸。其他旨在支持客户在网上找到适合它们的东西的最先进的方法大多需要高水平的客户参与和隐私敏感数据(如身高、体重、年龄、性别、腹部形状等),或者需要衣着紧身的客户身体的图像。它们也往往缺乏能力,无法在规模上制作适合和形成有意识的视觉指导,短短的办法是简单地建议哪些尺寸最适合客户的物理属性,而没有提供任何关于服装如何适合和外观的信息。其他最先进的方法,我们介绍的是,一个符合现实的对口味的对口服尺寸和对口服结构的数据模型,我们从现实的角度来理解和对在线时装的特征的描述。